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Tomography, Optical Coherence

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Rupture Detection During Needle Insertion Using Complex OCT Data and CNNs.

IEEE transactions on bio-medical engineering
OBJECTIVE: Soft tissue deformation and ruptures complicate needle placement. However, ruptures at tissue interfaces also contain information which helps physicians to navigate through different layers. This navigation task can be challenging, wheneve...

Deep learning models for benign and malign ocular tumor growth estimation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Relatively abundant availability of medical imaging data has provided significant support in the development and testing of Neural Network based image processing methods. Clinicians often face issues in selecting suitable image processing algorithm f...

Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study.

The Lancet. Digital health
BACKGROUND: Geographic atrophy is a major vision-threatening manifestation of age-related macular degeneration, one of the leading causes of blindness globally. Geographic atrophy has no proven treatment or method for easy detection. Rapid, reliable,...

Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation.

Journal of biophotonics
Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter penetration. However, the high scattering of the skull and the strong noise in the deep imaging regi...

Imaging and artificial intelligence for progression of age-related macular degeneration.

Experimental biology and medicine (Maywood, N.J.)
Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which m...

Attention-based deep learning system for automated diagnoses of age-related macular degeneration in optical coherence tomography images.

Medical physics
PURPOSE: The progression of age-related macular degeneration (AMD) is critical to treatment decisions in clinical practice. The disease can be classified into four categories namely, drusen, inactive choroidal neovascularization (CNV), active CNV, an...

Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole.

The British journal of ophthalmology
AIMS: To develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month afte...

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy.

Computers in biology and medicine
BACKGROUND: In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-bas...

Deep-learning-based motion correction in optical coherence tomography angiography.

Journal of biophotonics
Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visuali...